No Priors: Artificial Intelligence | Technology | Startups

What does it mean to build wealth as an individual investor in today’s society, especially as gains continue to get concentrated into smaller groups of VCs or institutional investors? Robinhood’s CEO and co-founder Vlad Tenev has a vision that can be summed up in one word: Ownership. In this episode, Sarah Guo chats with Vlad to get a closer look at how Robinhood has evolved from a simple app to buy stocks at zero commission into a global conglomerate that allows customers to own across a wide variety of surfaces and asset classes. Vlad explains how Robinhood is helping users extend ownership amidst the current economy and ongoing AI revolution through Trump accounts, tokenization of privates, and newer product offerings like Robinhood Chain, Agentic Trading, and Agentic Credit Card. He also talks about how his new mathematical superintelligence startup, Harmonic, is revolutionizing the field and how he hopes it will decentralize new discoveries and inventions. Vlad and Sarah also discuss how prediction markets could tie into the ownership conversation, whether financial infrastructure can be optimized to enable even higher volumes of high-frequency trading, and his advice for maintaining a level head when facing the vagaries of a changing financial market.  

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Chapters: 

00:46 – Intro to Vlad Tenev, His Background, and Robinhood Today

12:25 – A Revolutionary Future for Mathematics

18:49 – Robinhood’s Vision for Tokenization

26:06 – Monetizing Knowledge via Prediction Markets

28:48 – Impacts of Agentic Trading and Democratizing Trading

33:48 – Safeguarding Financial Infrastructure for a New Load of HFTs

38:30 – Ownership of Private Companies in the AI Revolution

48:34 – Robinhood and Trump Accounts 

50:47 – Keeping a Steady Hand and Head as a Leader

53:48 – Conclusion

What happens when you put code-generation agents in the hands of enterprise IT teams? With Serval, you get a system where it's easy for everyone to be a builder. Sarah Guo and Elad Gil sit down with Serval co-founder and CEO Jake Stauch to analyze his company’s direct challenge to ServiceNow as the enterprise automation and service management system of record. Jake explains that by building an AI platform for enterprise IT departments with Serval, users can safely build while still safeguarding permissions by strict deterministic approvals and an audit trail. He also talks about ProServal, the company’s team that embeds automation engineers directly with customers in order to provide ongoing support and service. Jake also discusses how AI was the big unlock to taking on ServiceNow, why he’s betting on coding agents getting even better, why consumer is a window into what will happen next in enterprise and why the SaaS-pocalypse might look more like a consolidation, why background agents are still a moonshot in enterprise software, and the importance of hiring product-oriented engineers.

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Chapters:

00:47 – Jake Stauch Introduction and Serval’s Background 

03:00 – Expanding Coding Agents Beyond Engineers

04:40 – Limitations with Personal Agents in Enterprise

06:29 – Deploying Serval Effectively in Large Orgs 

07:55 – Day 1 Ambition and Attacking Enterprise Systems of Record

09:51 – Taking on ServiceNow with Workflow Automation Platform

11:01 – Betting on the Evolution of Coding Agents and Building Guardrails in Enterprise

13:48 – Big Risks, Big Rewards, and Big Predictions for Serval and Coding Agents

16:40 – Adopting Different Sets of Building Principles at an Enterprise Level

20:49 – Token Maxing, Long-Term Budget Impacts, and ROI

22:39 – The SaaS-pocalypse and SaaS vs. AI

24:39 – The Mental Model for Delivering Platform-Level Power to the IT User at Enterprise

26:43 – Building Automations with Catalyst and Raising Technical Competence

28:55 – The Next Wave: Background Agents and Autonomy in Enterprise

32:23 – Hiring Velocity and Unlimited Ambition at Serval

34:17 – Keeping Pace with Customers and Fundamental Shifts in Product Function

37:05 – Conclusion

In the transition to the agentic web, security has become an afterthought. Now, organizations are racing to figure out what AI security means, and how to keep their data safe. Live from Cloudflare Connect, Sarah Guo sits down with Cloudflare CTO Dane Knecht to discuss the changing attacker landscape, and how his company is allocating its engineering resources to meet this emerging security challenge. Dane explains how enterprises that have moved away from castle-and-moat infrastructure to more of a zero trust infrastructure for security are in better positions than those who haven’t. He also discusses the challenges of patching 70 years of software and why the software development lifecycle will need to change for security for the agentic web, why he sees token spend in the next 18 months primarily going towards security rather than engineering, how the current web is in a ‘messy middle’ state where humans and agents are riding the same ‘rails’ on the internet, Cloudflare’s conference monetization gateway release as well as its Signals project with OpenAI, the rationale behind the release of Cloudflare OS, and the thinking behind a recent company restructuring. Plus, why Cloudflare hired one thousand interns, and why curiosity about AI is required to be successful at Cloudflare. 

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Chapters:

00:00 – Trailer

00:55 – Cloudflare CTO Top Priorities

05:03 – Today’s Attacker Landscape

06:16 – State of Internet Infrastructure

07:31 – Shift Towards Security Spend

08:39 – Road to Better Software and Internet Security

09:50 – Building Secure Production Software

11:41 – Reducing Surface Area

12:54 – Near Future of the Web

17:27 – Monetization and Signals 

19:13 – Success for Content Creators 

20:07 – Workplace OS and Use Cases

24:07 – Restructuring for AI-First

27:11 – Token Budgeting

29:32 – Cloudflare’s Intern Strategy

33:39 – Importance of Curiosity

35:29 – Attacking the Legacy Software Security Problem

37:08 – Engineering Team Prioritization

38:25 – Open vs. Closed Models

39:43 – Democratizing Security for Everyone

41:29 – Conclusion

Adversaries are already running AI agents, and the fastest attacks now play out in seconds. Sarah Guo and Elad Gil sit down with CrowdStrike president and CEO George Kurtz on what AI-equipped attackers mean for enterprise security. George explains how agents are flattening the adversary pyramid by putting advanced tradecraft in the hands of low-skill attackers, and why CrowdStrike and Nvidia are building a frontier-style lab of their own to train security models. They also get into how open-weight and closed models compare on security, where CrowdStrike is directing its platform investment, lessons from the recent Hugging Face incident, and why George sees AI, like every platform shift before it, as a net creator of opportunity.

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Chapters:

00:00 – Trailer

00:59 – George Kurtz Introduction

01:24 – Agentic Landscape

02:45 – AI and Adversaries

04:51 – Open Weight Models and Adversarial Attacks

06:37 – Adversary Geography

08:34 – Attacker Capability in Context

11:24 – Hugging Face Takeaways

13:59 – CrowdStrike Frontier Security Models

16:32 – CrowdStrike Key Offerings

19:51 – What Differentiates CrowdStrike’s Models 

23:57 – Near Term State of Security World

25:46 – State of Model Distillation

27:53 – Open Weight Model Importance and Cost

29:59 – Assessing Real Agent Threat

32:40 – Areas to Watch in AI

35:02 – Case for Defense Dominance

39:15 – Executive Advice

43:16 – Shifts Create Opportunity

45:27 – Conclusion

Technology has always been the fundamental driver of improvement for human quality of life. And with the recent rollout of advanced frontier models, like OpenAI’s GPT-6, humanity can now massively accelerate scientific discovery for its benefit. Sarah Guo and Elad Gil are joined by OpenAI President and co-founder Greg Brockman to discuss the company’s rapid progress across frontier models, cybersecurity, and scientific discovery. Greg shares how nearly 400 century-old math problems have been solved using its AI agents, and how the technology has the potential to transform other fields like biology in the realm of drug discovery. He also talks about how proactive, always-on AI agents like Dots impact modern productivity, why he believes the best UI is no UI at all, how cybersecurity is in a critical window of time to use peak intelligence to find and fix vulnerabilities in systems, and why OpenAI remains committed to keeping humans in the driver’s seat even as models surpass us in capability.

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Chapters:

00:42 – Trailer

00:59 – Greg Brockman Introduction

01:11 – AI and Advancements in Mathematics

05:55 – AI Communication and Oversight

07:45 – AI for Overcoming Biology Data Bottlenecks

11:28 – Revolutionizing Drug Discovery

14:18 – Changing Regulations to Accelerate Progress

16:51 – Dots the Proactive Agent

19:19 – Unifying Work and Consumer Agents

22:02 – How UI Will Evolve

26:08 – Cybersecurity in the AI Age

32:55 – Shift in Security Investment

35:49 – OpenAI’s Human-Centric Ethos

38:28 – Conclusion

Is the future of frontier AI open or closed? ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo and Elad Gil to discuss the launch of Beam, the company’s 500 billion parameter open-weight reasoning model. Misha breaks down the pre-training and reinforcement learning required to produce Beam’s reasoning efficiency, the shift in enterprise compute from renting to owning intelligence, and how he believes that open models will capture the majority of global token demand. He also talks about the open model ecosystem in China and why competition with China’s models is a good thing, safety considerations around open models, and how frontier-level open models may accelerate the pace of scientific discovery.  

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Chapters:

00:42 – Misha Laskin Introduction

00:59 – Latest with ReflectionAI

02:07 – Challenges Building an Open Model

03:56 – ReflectionAI’s Agentic Shift

07:37 – Resources for Model Training

10:09 – Scaling Efficiency

12:18 – Training Beam

16:51 – Where Model Value Comes From

19:55 – Beam’s Reasoning Efficiency

22:35 – Monetizing Open Weight Models

25:03 – Future of Open Versus Closed Tokens

31:29 – Competition with Open Models

34:38 – Chinese Open Source Model Ecosystem

38:40 – Will Chinese Models Remain Open

44:44 – Safety and Open Models

53:02 – Debating Access to Powerful Tools

57:16 – AI and Scientific Progress

01:00:36 – Data Centers and Jobs

01:02:07 – Beam and Scientific Research

01:05:37 – Research Head Count to Compute Ratio

01:10:29 – Conclusion

Founder and CEO of full-stack AI chip company Fractile, Walter Goodwin, joins Sarah Guo to discuss the bets he’s made on the future of the chip market as other major players like Broadcom, NVIDIA, and AMD try to accelerate their workloads. They discuss the difference in Fractile’s newer approach on model architecture with a full-stack team in the current chip landscape and the technical bets they’re making in that direction. Walter also talks about compressing the gap between the chip design cycle and its payoff period, and making a generational leap in AI inference to realize the bet in volume against the value to be captured. 

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Chapters:

00:46 – Walter Goodwin and Fractile Introduction 

02:29 – The Chip Landscape Now

04:56 – Common Handoffs From Architecture-Focused Players

07:18 – Full Stack Approach and Team Setup

09:49 – Fractile’s Most Important Technical Bets 

15:32 – Workload Predictions and Compressing the Chip Design Cycle

23:03 – Architect Intent to Output Bottlenecks and Accelerating Trials

28:16 – Workload Bets on Model Architectural Shifts

31:20 – The Future of AI Chip Players and Market Structure  

35:14 – Conclusion

Can AI transform legacy incumbents rather than replacing them? Sequence Holdings co-founder and CEO Michael Lee joins Sarah Guo to discuss how holding company structures and engineering integrations are reshaping market leaders from the inside out. Michael details Sequence’s $7.7 billion take-private transaction of Baldwin alongside Dell Family Office (DFO), and shares his thesis on why traditional consulting models and software sales fall short for real enterprise AI transformations. They also talk about why permanent holding company structures are good for long-term compounding, real-world results from applying frontier engineering to BankSouth, and Michael’s lessons from his time in public investing, private equity, and operating at the intersection of market incumbents and AI.

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Chapters:

00:34 – Michael Lee Introduction

01:03 – Sequence Holdings and Baldwin

01:54 – Idea for Sequence

04:36 – Incumbents in the AI Era

06:32 – Why a Holding Company

11:21 – Recruiting Top Engineers

13:08 – Investing in BankSouth

17:46 – Why an Insurance Brokerage

20:17 – Atlas Platform Explained

23:53 – Traditional Private Equity Limitations

27:23 – What Sequence Looks For in Management Teams

31:04 – Accomplishments at BankSouth

34:45 – Founder Lessons

36:08 – Story of Dell Partnership

37:10 – Career and Investment Approach

40:00 – Value of Exceptional People

42:23 – Conclusion

As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency. 

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Chapters:

00:00 – Stefano Ermon Introduction

00:35 – Research Background

02:54 – Starting Inception

05:59 – Why Diffusion Beats Autoregressive

11:10 – Discrete vs. Continuous Modalities

13:19 – Inception Today

16:45 – Where Speed Wins

17:31 – Inception Customer Base

18:49 – Interaction with Hardware Landscape

19:34 – Inception and the Broader Industry

21:41 – Data Compression and Structure

24:45 – Controllability of Diffusion Modeles

27:25 – Emergent Capabilities at Scale

29:02 – Future Workload Split Between Diffusion vs. Traditional

30:03 – Adoption Challenges

31:44 – Hiring and Team Organization

32:50 – Recursive Self Improvement

34:02 – Resource Allocation

35:10 – Impact of Academia

38:13 – Conclusion

From the tokenization of real-world assets to funding research on anti-aging therapeutics, Coinbase co-founder and CEO Brian Armstrong is focused on solving meta-problems for the benefit of society. Brian joins Elad Gil this episode to discuss the intersections of artificial intelligence, crypto rails, and biotechnology. He details Coinbase’s vision for agentic finance, as well as how Coinbase is becoming the ‘Everything Exchange’, which includes tokenized real-world stocks, stablecoin payment adoption, and prediction markets. Brian also introduces his new venture, New Limit, a company founded to tackle longevity through epigenetic reprogramming, and outlines initial therapeutic programs and the roadmap toward clinical trials.  

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Chapters:

00:00 – Cold Open Trailer

00:45 – Brian Armstrong Introduction

01:13 – Coinbase and the Everything Exchange

05:52 – Agentic Commerce

10:00 – AI and Crypto

10:49 – AI Inside Coinbase

15:39 – Productivity and Company Size

17:26 – The Everything Exchange and Tokenization

20:53 – Prediction Markets

23:15 – Introducing New Limit

34:17 – Looking Forward Five Years

40:24 – Special Economic Zones

44:26 – Conclusion